The analysis of human activities is necessary for different applications, like medical assessments, smart environments, and sports. Sports applications are of special interest for both professional and recreational use. Sports applications include coordination and performance analysis of athletes. Monitoring athletes while training or competing is essential to providing pertinent feedback. Furthermore, a feedback system may motivate a person to increase daily sports activities and improve individual fitness.
For example, athletes performing in a sports match desire a video summary of their performances, such as a video showing their soccer kicks or tricks, to obtain feedback and use to improve their abilities. However, watching the entire sports activity, such as a soccer match, and cutting the relevant sequences manually for each player in order to provide him with a video summary is a time-consuming task. Thus, this kind of analysis is done only in the professional area (for example, prime league soccer clubs) where a staff of trainers and assistants supports the athletes. In other areas of mass and recreational sports, these kinds of video analyses and video summaries are not produced because of their time-consuming and elaborate nature.
On the other hand, body worn inertial-magnetic sensors also may be used to analyze human activities. Body worn inertial-magnetic sensors may capture kinematics of human motion by evaluating the movement of an integrated mass. This movement is caused by inertial forces. Alternatively, these sensors may capture orientation of human motion by evaluating the sensor's orientation with respect to external magnetic fields. Such sensors offer benefits like miniaturization, being light-weight, and being inexpensive. These sensors may be integrated in sportswear and acquire movement and orientation data over a long period of time in unconstrained environments. Data acquired by such sensors may be processed either in real-time (online processing), or may be stored for later processing when the particular activity is finished (off-line processing).
An example of real-time processing is an athlete wearing a body sensor network including inertial and/or magnetic sensors and measuring the accelerations and angular velocities at different parts of the athlete's body. Acquired data is transmitted to a mobile device by wireless technology like Bluetooth®, BTLE (Bluetooth low energy), WLAN, ZigBee®, ANT®, or Ant+. The mobile device may evaluate and interpret the data using real-time processing. Parameters like the number of steps, step sizes, running speed, running distance, and speed. Energy expenditure of the athlete may be computed based on the data, and performance feedback may be provided to the athlete by vision, speech, or vibration. Real-time processing demands feedback until a certain timestamp.
For off-line processing, data from the sensors is stored in a memory. For example, the sensors may be connected to a data logger by thin wires. Alternatively, the sensors may transmit the data to the data logger wirelessly, for example, by any of the wireless technologies mentioned above. After the activity, the data logger is connected to a computer, for example, via USB or any other suitable connection, and the data is transferred to the computer for off-line processing. The data is then processed on the computer to obtain, for example, running distance, speed, or energy expenditure.
Current online and off-line processing methods of sensor-based data quantify activities, which last over a long period of time, like running. Thus, these processing methods do not quantify short, frequently occurring events in sports, like kicking in soccer, tennis strokes, or lay-ups in basketball. An event is defined as a part of a human activity that has a short and restricted duration.
In sum, video based systems used to analyze human activity, particularly a sports activity, and used to provide a video feedback or summary are time-consuming and limited to the professional area. Moreover, current methods that process data acquired from body-worn sensors deliver summary statistics relating to an entire activity (for example, a match), but do not focus on particular events (such as shots). Furthermore, it is not possible for current processing methods to analyze a motion sequence (such as a kick) in detail from data acquired from body-worn sensors.
For example, US 2012/123733 A1 relates to a method for human movement recognition comprising the steps of: retrieving successive measuring data for human movement recognition from an inertial measurement unit; dividing the successive measuring data to generate at least one human movement pattern waveform if the successive measuring data conforms to a specific human movement pattern; quantifying the at least one human movement pattern waveform to generate at least one human movement sequence; and determining a human movement corresponding to the inertial measurement unit by comparing the at least one human movement sequence and a plurality of reference human movement sequences.
US 2012/0167684 A1 discloses a selective motion recognition apparatus using an inertial sensor. The selective motion recognition apparatus using an inertial sensor includes: a sensor unit; a selection unit that outputs a sensor selection signal; and a motion detection unit that receives angular velocity sensor data and acceleration sensor data output from the sensor unit.
U.S. Pat. No. 8,702,516 B2 is directed to the recognition of events within motion data including, but not limited to, motion capture data obtained from portable wireless motion capture elements, such as visual markers and sensors, radio frequency identification tags, and motion sensors within mobile device computer systems, or motion data calculated based on analyzed movement associated with a same user, another user, a historical user or a group of users.
According to US 2013/0274635 A1, a sensor module is physically coupled to an object during an athletic activity of a user. An athletic activity monitoring method for use with the sensor module includes the steps of detecting movement of the object, recording movement data, identifying a matching athletic motion from a plurality of reference motions by comparing the movement data to data associated with the plurality of reference motions, and providing an output to the user that identifies of the matching athletic motion.
US 2013/0274904 A1 is directed to a method for monitoring an individual engaged in athletic activity, including detecting movement of the individual at a first time, using a sensor module coupled to the individual, determining that the movement of the individual corresponds to a predetermined activation movement, entering an active state of the sensor module in response to the determination that the movement of the individual corresponds to the predetermined activation movement, and detecting movement of the individual at a second time, when the sensor module is in the active state.
It is therefore an object of the present invention to provide a method for providing a video summary of an activity of a person, in particular a sports activity, which is simple and fast, and provides the video summary immediately after the activity, without requiring time-consuming manual cutting of videos. A further object of the present invention relates to providing a corresponding system.